Filawati et al., 2025 - Google Patents
with Long Short-Term Memory NetworkFilawati et al., 2025
- Document ID
- 14048412501466661591
- Author
- Filawati S
- Fani S
- Risdianto D
- Publication year
- Publication venue
- Proceedings of the 10th International Seminar on Aerospace Science and Technology; ISAST 2024; 17 September, Bali, Indonesia: Integrating Aviation, Aerospace Science and Technology for Climate Solution
External Links
Snippet
Satellites in the geostationary orbit are located in the outermost radiation belt area and are vulnerable to space weather conditions, such as high ener-getic electron interactions, which can cause damage to satellite components. It is important to predict electron flux in this orbit …
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS
- G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Camporeale et al. | Machine learning techniques for space weather | |
| CN100524288C (en) | Space weather prediction system and method | |
| US6816786B2 (en) | Space weather prediction system and method | |
| Tebabal et al. | Local TEC modelling and forecasting using neural networks | |
| Chen et al. | PreMevE: New predictive model for megaelectron‐volt electrons inside Earth's outer radiation belt | |
| Wei et al. | Quantitative prediction of high‐energy electron integral flux at geostationary orbit based on deep learning | |
| CN113139327B (en) | Ionized layer TEC single-point prediction method and system based on GRU network model | |
| Shenvi et al. | Forecasting of ionospheric total electron content data using multivariate deep LSTM model for different latitudes and solar activity | |
| Sakaguchi et al. | Prediction of MeV electron fluxes throughout the outer radiation belt using multivariate autoregressive models | |
| Şentürk et al. | A Multi-Network based Hybrid LSTM model for ionospheric anomaly detection: A case study of the Mw 7.8 Nepal earthquake | |
| Tian et al. | Estimation model of global ionospheric irregularities: An artificial intelligence approach | |
| Kunduri et al. | An examination of SuperDARN backscatter modes using machine learning guided by ray‐tracing | |
| Knight et al. | Auroral ionospheric E region parameters obtained from satellite‐based far ultraviolet and ground‐based ionosonde observations: Data, methods, and comparisons | |
| Filawati et al. | with Long Short-Term Memory Network | |
| Aleshko et al. | Development of Methodology for Predicting Space Weather Based on Machine Learning Model | |
| Mao et al. | Calibrating approximate Bayesian credible intervals of gravitational-wave parameters | |
| Laurenza et al. | Upgrades of the ESPERTA forecast tool for solar proton events | |
| Salasa et al. | The Impact of Satellite Position and Derived Parameters on Electron Flux Prediction with Long Short-Term Memory Network | |
| Benella et al. | Statistical treatment of solar energetic particle forecasting through supervised learning approaches | |
| Eroglu et al. | Bézier cubics and neural network agreement along a moderate geomagnetic storm | |
| Kourogiorgas et al. | Long-term and short-term atmospheric impairments forecasting for high throughput satellite communication systems | |
| Farid et al. | Singular spectrum analysis and modeling of the temporal variations of the global ionospheric F2-layer peak electron density retrieved from COSMIC radio occultation measurements | |
| Ünal et al. | Performance of IRI-based ionospheric critical frequency calculations with reference to forecasting | |
| Ellis | Detection and Characterization of Ionospheric Sporadic E: A Machine Learning Approach | |
| Yang et al. | Exploring Bayesian deep learning for weather forecasting with the Lorenz 84 system |